Q: How would you characterize your agency’s current and/or planned use of predictive analytics? What is one specific way in which predictive analytics actively drives decisions in your agency?

A: A core function of the Government Accountability Office (GAO) is to use data to objectively evaluate government programs and spending. We both develop our own models and evaluate agencies’ own models to evaluate the effectiveness of government programs. For example, we have completed numerous evaluations of Medicare payment policies to evaluate possible effects on quality and access to care.

Q: Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: The biggest problem we face is acquiring and validating data from the agencies we audit. Data are provided in multiple formats and often have quality issues. These include duplicate, incomplete, or inaccurate entries, and inconsistencies among records. This challenge is compounded when we merge data from multiple agencies or multiple sources to conduct more sophisticated analyses.

Q: Can you discuss any near term goals you have for improving your agency’s use of predictive analytics?

A: There is an increasing awareness across our analyst community of the value of predictive analytics to measure the effectiveness of federal programs. This includes training and outreach efforts from our analytics experts to other agency staff. We are also taking greater advantage of open source tools for analytics and developing strategies to increase the amount of staff that can help with the initial stages of data acquisition, evaluation, and preparation.

Q: Can you describe a successful result from the employment of predictive analytics in your agency, i.e., cost avoidance, funds recovered, improved efficiency, etc.

A: We analyzed TSA’s program to detect more risky air travel passengers and determined it was not based on sound evidence. In another case, we evaluated a Department of Transportation program that used a large amount of data to calculate safety scores for commercial trucking carriers and to ostensibly determine the likelihood of crashes. We found that violations used in the agency model either occurred too infrequently to be useful, or were not otherwise predictive of crashes.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Government.

A: To better understand the circumstances faced by those who claim early Social Security benefits, GAO examined demographic and occupational characteristics associated with early claiming. More specifically, we examined the characteristics and income of early claimers using data from the Health and Retirement Study. I look forward to discussing our findings in detail at the session.